Folder Path

/Misc/Deep Learning Course Handouts/

0 directories 64 files 105 MiB total
List Grid
Name
Size Modified
Up
by-nc-sa-4.0.txt
20 KiB
dlc-handout-1-1-from-anns-to-deep-learning.pdf
5.6 MiB
dlc-handout-1-2-current-success.pdf
7.8 MiB
dlc-handout-1-3-what-is-happening.pdf
5.6 MiB
dlc-handout-1-4-tensors-and-linear-regression.pdf
216 KiB
dlc-handout-1-5-high-dimension-tensors.pdf
485 KiB
dlc-handout-1-6-tensor-internals.pdf
81 KiB
dlc-handout-10-1-autoregression.pdf
380 KiB
dlc-handout-10-2-causal-convolutions.pdf
514 KiB
dlc-handout-10-3-NVP.pdf
1.5 MiB
dlc-handout-11-1-GAN.pdf
10 MiB
dlc-handout-11-2-Wasserstein-GAN.pdf
885 KiB
dlc-handout-11-3-conditional-GAN.pdf
8.7 MiB
dlc-handout-11-4-persistence.pdf
103 KiB
dlc-handout-12-1-RNN-basics.pdf
356 KiB
dlc-handout-12-2-LSTM-and-GRU.pdf
210 KiB
dlc-handout-12-3-word-embeddings-and-translation.pdf
389 KiB
dlc-handout-13-1-attention-memory-translation.pdf
759 KiB
dlc-handout-13-2-attention-mechanisms.pdf
366 KiB
dlc-handout-13-3-transformers.pdf
9.8 MiB
dlc-handout-14-draft.pdf
66 KiB
dlc-handout-2-1-loss-and-risk.pdf
195 KiB
dlc-handout-2-2-overfitting.pdf
1.4 MiB
dlc-handout-2-3-bias-variance-dilemma.pdf
600 KiB
dlc-handout-2-4-evaluation-protocols.pdf
120 KiB
dlc-handout-2-5-basic-embeddings.pdf
2.4 MiB
dlc-handout-3-1-perceptron.pdf
229 KiB
dlc-handout-3-2-LDA.pdf
664 KiB
dlc-handout-3-3-features.pdf
657 KiB
dlc-handout-3-4-MLP.pdf
217 KiB
dlc-handout-3-5-gradient-descent.pdf
2.5 MiB
dlc-handout-3-6-backprop.pdf
197 KiB
dlc-handout-4-1-DAG-networks.pdf
181 KiB
dlc-handout-4-2-autograd.pdf
214 KiB
dlc-handout-4-3-modules-and-batch-processing.pdf
207 KiB
dlc-handout-4-4-convolutions.pdf
213 KiB
dlc-handout-4-5-pooling.pdf
101 KiB
dlc-handout-4-6-writing-a-module.pdf
137 KiB
dlc-handout-5-1-cross-entropy-loss.pdf
260 KiB
dlc-handout-5-2-SGD.pdf
468 KiB
dlc-handout-5-3-optim.pdf
97 KiB
dlc-handout-5-4-l2-l1-penalties.pdf
367 KiB
dlc-handout-5-5-initialization.pdf
373 KiB
dlc-handout-5-6-architecture-and-training.pdf
472 KiB
dlc-handout-5-7-writing-an-autograd-function.pdf
132 KiB
dlc-handout-6-1-benefits-of-depth.pdf
394 KiB
dlc-handout-6-2-rectifiers.pdf
297 KiB
dlc-handout-6-3-dropout.pdf
1.0 MiB
dlc-handout-6-4-batch-normalization.pdf
460 KiB
dlc-handout-6-5-residual-networks.pdf
1.1 MiB
dlc-handout-6-6-using-GPUs.pdf
212 KiB
dlc-handout-7-1-transposed-convolutions.pdf
217 KiB
dlc-handout-7-2-autoencoders.pdf
456 KiB
dlc-handout-7-3-denoising-autoencoders.pdf
5.6 MiB
dlc-handout-7-4-VAE.pdf
1.3 MiB
dlc-handout-8-1-CV-tasks.pdf
3.9 MiB
dlc-handout-8-2-image-classification.pdf
709 KiB
dlc-handout-8-3-object-detection.pdf
2.2 MiB
dlc-handout-8-4-segmentation.pdf
6.7 MiB
dlc-handout-8-5-dataloader-and-surgery.pdf
111 KiB
dlc-handout-9-1-looking-at-parameters.pdf
3.4 MiB
dlc-handout-9-2-looking-at-activations.pdf
2.5 MiB
dlc-handout-9-3-visualizing-in-input.pdf
3.4 MiB
dlc-handout-9-4-optimizing-inputs.pdf
4.8 MiB